{{announcement.body}}
{{announcement.title}}

# Physical Constants in Python

DZone 's Guide to

# Physical Constants in Python

### Python and SciPy library are pretty powerful scientific tools. In this post, we put this to the test using a famous problem from the world of physics.

· Big Data Zone ·
Free Resource

Comment (0)

Save
{{ articles[0].views | formatCount}} Views

You can find a large collection of physical constants in `scipy.constants`. The most frequently used constants are available directly, and hundreds more are in a dictionary `physical_constants`.

The fine structure constant α is defined as a function of other physical constants:

The following code shows that the fine structure constant and the other constants that go into it are available in `scipy.constants`.

``````    import scipy.constants as sc

a = sc.elementary_charge**2
b = 4 * sc.pi * sc.epsilon_0 * sc.hbar * sc.c
assert( abs(a/b - sc.fine_structure) < 1e-13 )``````

## Eddington's Constant

In the 1930s, Arthur Eddington believed that the number of photons in the observable universe was exactly the Eddington number:

Since at the time the fine structure constant was thought to be 1/136, this made the number of photons a nice even 136 × 2 256. Later he revised his number when it looked like the fine structure constant was 1/137. According to the Python code above, the current estimate is more like 1/137.036.

Eddington was a very accomplished scientist, though he had some ideas that seem odd today. His number is a not a bad estimate, though nobody believes it could be exact.

## Related Posts

The constants in `scipy.constants` have come up in a couple previous blog posts.

The post on Koide's coincidence shows how to use the `physical_constants` dictionary, which includes not just the physical constant values but also their units and uncertainty.

The post on Benford's law shows that the leading digits of the constants in `scipy.constants` follow the logarithmic distribution observed by Frank Benford (and earlier by Simon Newcomb).

Topics:
big data ,python ,scientific python ,scipy ,tutorial

Comment (0)

Save
{{ articles[0].views | formatCount}} Views

Published at DZone with permission of John Cook , DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.